Industrial machinery is often dangerous to humans. Some machinery is dangerous unless it is completely shut down, while other machinery may have a variety of operating states, some of which are hazardous and some of which are not. In some cases, the degree of hazard may depend on the location or distance of the human with respect to the machinery. As a result, many “guarding” approaches have been developed to separate humans and machines and to prevent machinery from causing harm to humans. One very simple and common type of guarding is simply a cage that surrounds the machinery, configured such that opening the door of the cage causes an electrical circuit to place the machinery in a safe state. If the door is placed sufficiently far from the machinery to ensure that the human can't reach it before it shuts down, this ensures that humans can never approach the machinery while it is operating. Of course, this prevents all interaction between human and machine, and severely constrains use of the workspace.
More sophisticated types of guarding may involve, for example, optical sensors. Examples include light curtains that determine if any object has intruded into a region monitored by one or more light emitters and detectors, and 2D LIDAR sensors that use active optical sensing to detect the minimum distance to an obstacle along a series of rays emanating from the sensor, and thus can be configured to detect either proximity or intrusion into pre-configured two-dimensional (2D) zones. More recently, systems have begun to employ 3D depth information using, for example, 3D time-of-flight cameras, 3D LIDAR, and stereo vision cameras. These sensors offer the ability to detect and locate intrusions into the area surrounding industrial machinery in 3D, which has several advantages. For example, a 2D LIDAR system guarding an industrial robot will have to stop the robot when an intrusion is detected well beyond an arm's-length distance away from the robot, because if the intrusion represents a person's legs, that person's arms could be much closer and would be undetectable by the 2D LIDAR. However, a 3D system can allow the robot to continue to operate until the person actually stretches his or her arm towards the robot. This allows a much tighter interlock between the actions of the machine and the actions of the human, which facilitates many applications and saves space on the factory floor, which is always at a premium. Additionally, in complex workcells it can be very difficult to determine a combination of 2D planes that effectively monitors the entire space; 3D sensors properly configured, can alleviate this issue.
Because human safety is at stake, guarding equipment must typically comply with stringent industry standards. These standards may specify failure rates for hardware components and rigorous development practices for both hardware and software components. Standards-compliant systems must ensure that dangerous conditions can be detected with very high probability, that failures of the system itself are detected, and that the system responds to detected failures by transitioning the equipment being controlled to a safe state. Simply keeping humans and machines apart represents a far simpler guarding task than detecting unsafe conditions when humans actively work with machines that can injure them. However, the separation of human and machines is not always optimal for productivity. An example of a potential collaborative application is the installation of a dashboard in a car—the dashboard is heavy and difficult for a human to maneuver but easy for a machine, and attaching it requires a variety of connectors and fasteners that require human dexterity and flexibility to handle correctly. Conventional guarding systems are insufficiently granular in operation to reliably monitor such collaborative environments.
Existing 3D sensor systems offer the possibility of improved granularity in guarding systems. But 3D sensor systems can be difficult to configure as compared with 2D sensor systems. First, specific safety zones must be must be designed and configured for each use case, taking into account the specific hazards posed by the machinery, the motion and trajectory of the machinery, the possible actions of humans in the workspace, the workspace layout, and the location and field of view of each individual sensor. It can be difficult to calculate the optimal shapes of exclusion zones, especially when trying to preserve safety while optimizing floor space and system throughput, where one object may present an occlusion relative to a sensor, and where some objects may be out of range or undetectable to the sensor.
Mistakes in the configuration can result in serious safety hazards, requiring significant overhead in design and testing. All of this work must be completely redone if any changes are made to the workspace. The extra degree of freedom presented by 3D systems results in a much larger set of possible configurations and hazards. Accordingly, a need exists for improved and computationally tractable techniques for monitoring a 3D workspace with high granularity.